International aid may take the form of multilateral aid – provided through international bodies such as the UN, or NGOs such as Oxfam – or bilateral aid, which operates on a government-to-government basis. There is considerable debate about whether international aid works, in the sense of reducing poverty and stimulating development.
However, the effectiveness of aid is often diluted by corruption. Aid is invariably channeled through the governments of recipient countries, in which power is often concentrated in the hands of a few politicians and bureaucrats, and the mechanisms of accountability are, at best, poorly developed. This tends to benefit corrupt leaders and elites rather than the people, projects and programs for which it was intended.
Watts, Carl. (2014). Re: Does foreign aid help the developing countries towards development?. Retrieved from: https://www.researchgate.net/post/Does_foreign_aid_help_the_developing_countries_towards_development/5322005ed039b1e7648b459c/citation/download.
The hypothesis that foreign aid can promote growth in developing countries was explored, using panel data series for foreign aid, while accounting for regional differences in Asian, African, Latin American, and the Caribbean countries as well as the differences in income levels, the results of this study also indicate that foreign aid has mixed effects on economic growth in developing countries.
Ekanayake, E. & Chatrna, Dasha. (2010). The effect of foreign aid on economic growth in developing countries. Journal of International Business and Cultural Studies. 3.
This study examines the relationships between foreign aid, institutional structure, and economic performance for 80 countries in Europe, America, Africa, and Asia. It is found that official development assistance and the quality of institutional structure in the sample countries affect economic growth positively.
Hayaloğlu, Pınar. (2023). Foreign Aid, Institutions, and Economic Performance in Developing Countries. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi. 18. 748-765. 10.17153/oguiibf.1277348.
Algunas librerias y paquetes usados para obtener y descargar los datos
library(tidyverse) # manejo de dataframes
Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
Warning: replacing previous import ‘lifecycle::last_warnings’ by ‘rlang::last_warnings’ when loading ‘hms’── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
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x dplyr::lag() masks stats::lag()
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library(reshape2) # para tranfromar data de long a wide
Attaching package: ‘reshape2’
The following object is masked from ‘package:tidyr’:
smiths
library(WDI) # libreria para acceder a metadata de banco mundial
library(readxl) # leer archivos de excel
library(readr) # leer archivos csv
library(visdat) # visualizacion de datos como graficos
library(plotly) # graficos
Attaching package: ‘plotly’
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last_plot
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filter
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layout
library(purrr) # funcion map
library(plm) # modelos lineales para datos panel
Attaching package: ‘plm’
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between, lag, lead
library(car) # test y utilidades para modelos
Loading required package: carData
Attaching package: ‘car’
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recode
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library(htmltools) # para imprimir graficos en html
Datos para paises bajos ingresos sean utilizados, segun clasificación del banco mundial, hay 26 paises de bajos ingresos y 51 de ingresos medios bajos
country_class <- read_excel("CLASS.xlsx")
country_class %>%
filter(!is.na(Region), !is.na(`Income group`)) %>%
group_by(`Income group`) %>%
summarise(countries = n()) %>%
arrange(factor(`Income group`, levels = c('High income', 'Upper middle income', 'Lower middle income', 'Low income')))
Listado de paises a analisar:
my_countries <- country_class %>%
filter(!is.na(Region), `Income group` %in% c('Low income', 'Lower middle income')) %>%
select(Code)
my_countries %>% merge(country_class) %>% select(Code, Economy)
Hacer la respectiva asociacion de nombres iso3c e iso2c
my_countries$iso2c <- WDI_data$country %>%
filter(iso3c %in% my_countries$Code) %>%
.$iso2c
my_countries
Datos del banco mundial (para ODA y los indices de gobernanza) y el Human Development Reports API son descargados desde scripts de Python. Son almacenados en archivos CSV y luego son cargados aqui:
datos_HDI <- read_csv("datos_python_HDI.csv", col_names = c('Code', 'iso2c', 'indicator', 'year', 'value'),
col_types = list(col_character(), col_character(), col_character(), col_double(), col_double()))
hdi_indicators <- datos_HDI %>% distinct(indicator) %>% .$indicator
oda_indicators <- c(
'DT_ODA_ALLD_CD',
'DT_ODA_ALLD_KD',
'DT_ODA_OATL_CD',
'DT_ODA_OATL_KD',
'DT_ODA_ODAT_CD',
'DT_ODA_ODAT_GI_ZS',
'DT_ODA_ODAT_GN_ZS',
'DT_ODA_ODAT_KD',
'DT_ODA_ODAT_MP_ZS',
'DT_ODA_ODAT_PC_ZS',
'DT_ODA_ODAT_XP_ZS'
)
gob_indicators <- c(
'CC_EST',
'CC_NO_SRC',
'CC_PER_RNK',
'CC_PER_RNK_LOWER',
'CC_PER_RNK_UPPER',
'CC_STD_ERR',
'GE_EST',
'GE_NO_SRC',
'GE_PER_RNK',
'GE_PER_RNK_LOWER',
'GE_PER_RNK_UPPER',
'GE_STD_ERR',
'PV_EST',
'PV_NO_SRC',
'PV_PER_RNK',
'PV_PER_RNK_LOWER',
'PV_PER_RNK_UPPER',
'PV_STD_ERR',
'RQ_EST',
'RQ_NO_SRC',
'RQ_PER_RNK',
'RQ_PER_RNK_LOWER',
'RQ_PER_RNK_UPPER',
'RQ_STD_ERR',
'RL_EST',
'RL_NO_SRC',
'RL_PER_RNK',
'RL_PER_RNK_LOWER',
'RL_PER_RNK_UPPER',
'RL_STD_ERR',
'VA_EST',
'VA_NO_SRC',
'VA_PER_RNK',
'VA_PER_RNK_LOWER',
'VA_PER_RNK_UPPER',
'VA_STD_ERR'
)
gdp_indicators <- c(
'NY_ADJ_NNTY_PC_CD',
'NY_ADJ_NNTY_PC_KD',
'NY_ADJ_NNTY_PC_KD_ZG',
'NY_GDP_PCAP_CN',
'NY_GDP_PCAP_KN',
'NY_GDP_PCAP_CD',
'NY_GDP_PCAP_KD',
'NY_GDP_MKTP_KD_ZG',
'NY_GDP_DEFL_ZS_AD',
'NY_GDP_DEFL_ZS',
'NY_GDP_MKTP_CD',
'NY_GDP_MKTP_CN',
'NY_GDP_MKTP_KN',
'NY_GDP_MKTP_KD',
'NY_GDP_PCAP_KD_ZG',
'NY_GDP_PCAP_PP_KD',
'NY_GDP_PCAP_PP_CD',
'SL_GDP_PCAP_EM_KD',
'SP_POP_GROW'
)
datos_WB <- data.frame(indicator = character(), iso2c = character(), year = double(), value = double())
suppressWarnings(
for (indicator in c(oda_indicators, gob_indicators, gdp_indicators)) {
datos_WB <- rbind(datos_WB, read_csv(paste("datos_python", indicator, ".csv", sep =''),
col_names = c('indicator', 'iso2c', 'year', 'value'),
col_types = list(col_character(), col_character(), col_double(), col_double())))
}
)
Poverty <- read_excel("GlobalExtremePovertyDollaraDay_Compact.xlsx", sheet = "Data Long Format")
names(Poverty) <- c("ccode", "country", "year", "value")
Poverty[Poverty=="Cape Verde"] <- "Cabo Verde"
Poverty[Poverty=="Congo"] <- "Congo, Rep."
Poverty[Poverty=="Egypt"] <- "Egypt, Arab Rep."
Poverty[Poverty=="Iran"] <- "Iran, Islamic Rep."
Poverty[Poverty=="Kyrgyzstan"] <- "Kyrgyz Republic"
Poverty[Poverty=="Laos"] <- "Lao PDR"
Poverty[Poverty=="Macedonia"] <- "North Macedonia"
Poverty[Poverty=="Russia"] <- "Russian Federation"
Poverty[Poverty=="Slovakia"] <- "Slovak Republic"
Poverty[Poverty=="South Korea"] <- "Korea, Rep."
Poverty[Poverty=="Swaziland"] <- "Eswatini"
Poverty[Poverty=="Syria"] <- "Syrian Arab Republic"
Poverty[Poverty=="The Gambia"] <- "Gambia, The"
Poverty[Poverty=="Turkey"] <- "Turkiye"
Poverty[Poverty=="Venezuela"] <- "Venezuela, RB"
Poverty[Poverty=="Yemen"] <- "Yemen, Rep."
Poverty <- Poverty %>%
filter(year > 1994) %>%
merge(WDI_data$country, all.x = TRUE) %>%
mutate(indicator = 'POV') %>%
merge(my_countries) %>%
select(indicator, iso2c, year, value)
datos_paper <- rbind(datos_WB, datos_HDI %>% select(indicator, iso2c, year, value), Poverty) %>%
pivot_wider(names_from = indicator, values_from = value)
datos_paper <- datos_paper %>% mutate(GOV = (CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST) / 6)
datos_paper <- datos_paper %>% arrange(iso2c, year) %>%
mutate(hdi_diff = case_when(iso2c == dplyr::lag(iso2c) ~ hdi - dplyr::lag(hdi), TRUE ~ NA_real_),
NY.GDP.PCAP.CD_diff = case_when(iso2c == dplyr::lag(iso2c) ~ NY.GDP.PCAP.CD - dplyr::lag(NY.GDP.PCAP.CD), TRUE ~ NA_real_),
DT.ODA.ALLD.CD_diff = case_when(iso2c == dplyr::lag(iso2c) ~ DT.ODA.ALLD.CD - dplyr::lag(DT.ODA.ALLD.CD), TRUE ~ NA_real_),
DT.ODA.ODAT.PC.ZS_diff = case_when(iso2c == dplyr::lag(iso2c) ~ DT.ODA.ODAT.PC.ZS - dplyr::lag(DT.ODA.ODAT.PC.ZS), TRUE ~ NA_real_),
GOV_diff = case_when(iso2c == dplyr::lag(iso2c) ~ GOV - dplyr::lag(GOV), TRUE ~ NA_real_),
POV_diff = case_when(iso2c == dplyr::lag(iso2c) ~ POV - dplyr::lag(POV), TRUE ~ NA_real_))
vis_dat(datos_paper %>% select(all_of(gsub("_", ".", oda_indicators))))
# DT.ODA.OATL.CD and DT.ODA.OATL.KD faltan
# DT.ODA.ODAT.GI.ZS, DT.ODA.ODAT.GN.ZS, DT.ODA.ODAT.MP.ZS and DT.ODA.ODAT.XP.ZS tienen faltas
# Un par de ocurrencias pais-año que faltan datos
vis_dat(datos_paper %>% select(NY.GDP.PCAP.CN, NY.GDP.PCAP.CD))
# NY.GDP.PCAP.CN, NY.GDP.PCAP.CD, NY.GDP.MKTP.CD, NY.GDP.MKTP.CN son buenos candidatos para usar como variables,
# 'SY'falta PIB per Capita en 2022, 2023 sin datos algunos paises
vis_dat(datos_paper %>% arrange(year) %>% select(all_of(gsub("_", ".", gob_indicators))))
# Datos del 2000 para atras tienen espacios faltantes
vis_dat(datos_paper %>% select(all_of(hdi_indicators)))
# abr, co2_prod, le, le_f, le_m, mmr son las pocas categorias sin datos faltantes
# hdi faltante en multiples ocaciones
vis_dat(datos_paper %>% arrange(iso2c) %>% select(SP.POP.GROW))
# ZW no tiene datos de crecimiento poblacional
vis_dat(datos_paper %>% arrange(iso2c) %>% select(POV))
# Hay muchos paises sin datos
# variables de etiqueta
ve <- c('iso2c', 'year')
# variables depndientes
vd <- c('hdi', 'hdi_diff', 'NY.GDP.PCAP.CD', 'NY.GDP.PCAP.CD_diff')
# variables independientes
vi <- c('DT.ODA.ALLD.CD', 'DT.ODA.ALLD.CD_diff', 'DT.ODA.ODAT.PC.ZS', 'DT.ODA.ODAT.PC.ZS_diff',
'SP.POP.GROW', 'GOV') #'CC.EST', 'GE.EST', 'PV.EST', 'RQ.EST', 'RL.EST', 'VA.EST', 'GOV_diff', 'POV', 'POV_diff'
# paises sin datos
delete_c <- c('SS', 'ZW', 'BT', 'ER', 'GW', 'KP', 'LB', 'NG', 'PS', 'SO', 'VU', 'FM', 'KI', 'TL', 'CV', 'SB', 'SY')
# años sin datos
first_y <- 2002
last_y <- 2022
datos_model <- datos_paper %>%
filter(!iso2c %in% delete_c, !year < first_y, !year > last_y) %>%
select(all_of(c(ve, vd, vi)))
datos_model
vis_dat(datos_model)
Se revisara las relaciones entre las variables graficamente No se ve una relacion clara, hay tanto paises con punteos altos y bajos de GOB que tienen tanto HID altos o bajos Quiza puede verse una leve relacion de mayor punteo en GOB acompañado de mejor punteo den HDI Los datos de GPD si muestran una relacion positiva con el HDI visto en las graficas
Probando modelos sencillos, regresion lineal, Minimos cuadrados, datos panel, HDI o GDP o sus differecias
summary(models[[keys[1]]])
Call:
lm(formula = f, data = datos_model)
Residuals:
Min 1Q Median 3Q Max
-0.23055 -0.05445 -0.00579 0.05112 0.56380
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.703e-01 6.959e-03 96.33 < 2e-16 ***
DT.ODA.ALLD.CD 1.115e-11 2.353e-12 4.74 2.38e-06 ***
SP.POP.GROW -4.422e-02 2.551e-03 -17.34 < 2e-16 ***
GOV 7.339e-02 5.517e-03 13.30 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.08798 on 1256 degrees of freedom
Multiple R-squared: 0.3091, Adjusted R-squared: 0.3074
F-statistic: 187.3 on 3 and 1256 DF, p-value: < 2.2e-16
HDI = ODA.ALL_diff + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[2]]])
# Todas las variables son significativas al 99% excepto ODA.ALL_diff y Regulatory Quality
HDI = ODA.PC + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[3]]])
# Todas las variables son significativas al 99% excepto Regulatory Quality
HDI = ODA.PC_diff + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[4]]])
# Todas las variables son significativas al 99% excepto ODA.ALL_diff y Regulatory Quality
HDI_diff = ODA.ALL + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[5]]])
# Todas las variables son significativas al 95% excepto ODA.ALL, Control of Corruption y Rule of Law
HDI_diff = ODA.ALL_diff + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[6]]])
# Todas las variables son significativas al 95% excepto Control of Corruption y Voice and Accountability
HDI_diff = ODA.PC + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[7]]])
# Todas las variables son significativas al 95% excepto Rule of Law y Voice and Accountability
HDI_diff = ODA.PC_diff + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[8]]])
# Todas las variables son significativas al 95% excepto Control of Corruption y Voice and Accountability
GPD.PC = ODA.ALL + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[9]]])
# Todas las variables son significativas al 99% excepto ODA.ALL, Control of Corruption, Regulatory Quality y Rule of Law
GPD.PC = ODA.ALL + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[10]]])
# Todas las variables son significativas al 99% excepto ODA.ALL_diff, Control of Corruption, Regulatory Quality y Rule of Law
GPD.PC = ODA.PC + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[11]]])
# Todas las variables son significativas al 99% excepto Rule of Law
GPD.PC = ODA.PC_diff + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[12]]])
# Todas las variables son significativas al 99% excepto ODA.ALL_diff, Control of Corruption, Regulatory Quality y Rule of Law
GPD.PC_diff = ODA.ALL + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[13]]])
# Todas las variables son significativas al 95% excepto ODA.ALL, Control of Corruption, Regulatory Quality y Voice and Accountability
GPD.PC_diff = ODA.ALL + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[14]]])
# Todas las variables son significativas al 95% excepto ODA.ALL_diff, Control of Corruption, Regulatory Quality y Voice and Accountability
GPD.PC_diff = ODA.PC + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[15]]])
# Todas las variables son significativas al 95% excepto ODA.PC, Control of Corruption, Political Stability, Regulatory Quality y Voice and Accountability
GPD.PC_diff = ODA.PC_diff + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[16]]])
# Todas las variables son significativas al 95% excepto ODA.PC_diff, Control of Corruption, Regulatory Quality y Voice and Accountability
plm(hdi ~ DT.ODA.ALLD.CD + GOV + SP.POP.GROW, data=datos_model,
index = c("iso2c", "year"), model = "within")
# summary(lm(hdi ~ DT.ODA.ALLD.CD + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW + iso2c, data=datos_model))
# Corruption Control, Government Effectiveness, Political Stability y Voice and Accountability no son significativas
plm(hdi ~ DT.ODA.ODAT.PC.ZS + GOV + SP.POP.GROW, data=datos_model,
index = c("iso2c", "year"), model = "within")
#summary(lm(hdi ~ DT.ODA.ODAT.PC.ZS + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW + iso2c, data=datos_model))
# Corruption Control, Government Effectiveness y Voice and Accountability no son significativas
plm(NY.GDP.PCAP.CD ~ DT.ODA.ALLD.CD + GOV + SP.POP.GROW, data=datos_model,
index = c("iso2c", "year"), model = "within")
#summary(lm(NY.GDP.PCAP.CD ~ DT.ODA.ALLD.CD + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW + iso2c, data=datos_model))
# Intercepto, Corruption Control, Regulatory Quality, Rule of Law, Voice and Accountability y Population Growth no son significativas
plm(NY.GDP.PCAP.CD ~ DT.ODA.ODAT.PC.ZS + GOV + SP.POP.GROW, data=datos_model,
index = c("iso2c", "year"), model = "within")
#summary(lm(NY.GDP.PCAP.CD ~ DT.ODA.ODAT.PC.ZS + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW + iso2c, data=datos_model))
# Corruption Control, Regulatory Quality, Rule of Law y Voice and Accountability no son significativas